Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Light Acquisition

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

TongueNet-GYN: a multimodal deep learning framework for non-invasive gynecological disease screening in digital public health.

Frontiers in public health·2026

Same author

L-Ornithine-L-aspartate enhances growth performance and nitrogen metabolism via modulation of intestinal amino acid transporters and microbiota in broilers.

Journal of animal science and biotechnology·2026

Same author

Synchronized Dietary Glucose and Amino Acid Supply Promotes Broiler Growth by Redirecting Metabolic Flux and Activating the AKT/mTORC1 Signaling Pathway.

Journal of agricultural and food chemistry·2026

Same author

Severe microvascular disease incidence over a 34-year period among Chinese with newly diagnosed diabetes and impaired glucose tolerance: the Da Qing diabetes outcome study 1986-2020.

Diabetology & metabolic syndrome·2026

Same author

Effect of transversus abdominis plane block on postoperative pain after nephrectomy: a systematic review.

Frontiers in medicine·2026

Same author

Enhanced Alkaline Water Electrolysis Using Cr-Doped NiCoP/Ni<sub>3</sub>S<sub>2</sub> Hollow Nanowires with Heterointerfaces as Bifunctional Catalysts.

Langmuir : the ACS journal of surfaces and colloids·2026

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

MOSSNet: multiscale and oriented sorghum spike detection and counting in UAV images.

Jianqing Zhao¹, Zhiyin Jiao^2,3, Jinping Wang^2,3

¹Key Laboratory for Climate Risk and Urban-Rural Smart Governance, School of Geography, Jiangsu Second Normal University, Nanjing, China.

Frontiers in Plant Science

|September 15, 2025

Summary

Accurate sorghum spike detection is crucial for crop monitoring and yield prediction. A new model, MOSSNet, effectively counts sorghum spikes in UAV images, outperforming existing methods in complex field conditions.

Keywords:

UAV angle feature deep learning oriented detection boxes sorghum spike

More Related Videos

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Related Experiment Videos

Last Updated: Jan 17, 2026

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

Agricultural Science
Computer Vision
Artificial Intelligence

Background:

Accurate sorghum spike detection is vital for crop monitoring, yield prediction, and food security.
Deep learning models offer improved accuracy but struggle with dense, variable sorghum spike features in UAV imagery.
Challenges include dense distribution, varied sizes, and complex backgrounds in aerial images.

Purpose of the Study:

To develop a robust model for accurate sorghum spike detection and counting in UAV images.
To enhance feature extraction for small and variably oriented sorghum spikes.
To improve the efficiency and accuracy of sorghum spike analysis in agricultural settings.

Main Methods:

Proposed MOSSNet (Multiscale and Oriented Sorghum Spike detection) model for UAV images.
Integrated Deformable Convolution Spatial Attention (DCSA) module for enhanced feature capture.
Employed Circular Smooth Labels (CSL) for morphological representation and Wise IoU loss for localization.

Main Results:

MOSSNet achieved 90.3% mAP in field conditions for sorghum spike counting.
Demonstrated superior performance in predicting spike orientation (RMSEa: 14.6, MAEa: 12.5).
Outperformed general object detection algorithms in counting accuracy (RMSE: 9.3, MAE: 8.1).

Conclusions:

MOSSNet effectively handles dense, occluded, and complex background scenes in sorghum spike detection.
The model shows robustness and generalizability for agricultural applications.
Future work includes exploring MOSSNet across different growth stages and developing real-time detection.